Simple Exponential Model Research Articles

Nonlinear models based on leaf architecture traits explain the variability of mesophyll conductance across plant species.

Mesophyll conductance ( ) describes the efficiency with which moves from substomatal cavities to chloroplasts. Despite the stipulated importance of leaf architecture in affecting , there remains a considerable ambiguity about how and whether leaf anatomy influences . Here, we employed nonlinear machine-learning models to assess the relationship between 10 leaf architecture traits and . These models used leaf architecture traits as predictors and achieved excellent predictability of . Dissection of the importance of leaf architecture traits in the models indicated that cell wall thickness and chloroplast area exposed to internal airspace have a large impact on interspecific variation in . Additionally, other leaf architecture traits, such asleaf thickness, leaf densityand chloroplast thickness, emerged as important predictors of . We also found significant differences in the predictability between models trained on different plant functional types. Therefore, by moving beyond simple linear and exponential models, our analyses demonstrated that a larger suite of leaf architecture traits drive differences in than has been previously acknowledged. These findings pave the way for modulating by strategies that modify its leaf architecture determinants.

Modeling and measurement of lead tip heating and resonant length for implanted, insulated wires.

To study implant lead tip heating because of the RF power deposition by developing mathematical models and comparing them with measurements acquired at 1.5 T and 3 T, especially to predict resonant length. A simple exponential model and an adapted transmission line model for the electric field transfer function were developed. A set of wavenumbers, including that calculated from insulated antenna theory (King wavenumber) and that of the embedding medium were considered. Experiments on insulated, capped wires of varying lengths were performed to determine maximum temperature rise under RF exposure. The results are compared with model predictions from analytical expressions derived under the assumption of a constant electric field, and with those numerically calculated from spatially varying, simulated electric fields from body coil transmission. Simple expressions for the resonant length bounded between one-quarter and one-half wavelength are developed based on the roots of transcendental equations. The King wavenumber for both models more closely matched the experimental data with a maximum root mean square error of 9.81°C at 1.5 T and 5.71°C at 3 T compared to other wavenumbers with a maximum root mean square error of 27.52°C at 1.5 T and 22.01°C for 3 T. Resonant length was more accurately predicted compared to values solely based on the embedding medium. Analytical expressions were developed for implanted lead heating and resonant lengths under specific assumptions. The value of the wavenumber has a strong effect on the model predictions. Our work could be used to better manage implanted device lead tip heating.

Forecasting the Turkish lira Exchange Rates Through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?

Forecasting the Turkish lira Exchange Rates Through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?

RMB/USD Exchange Rate Forecasting and Analysis by SES and ARIMA Model

The RMB/USD exchange rate is important in the worldwide financial sector. Predicting the future RMB/USD exchange rate is critical in such circumstances. Forecasting the RMB/USD exchange rate effectively can assist people in analyzing the economic situation and avoiding financial dangers. This research proposes two statistical models, Simple Exponential Smoothing (SES) model and Autoregressive Integrated Moving (ARIMA) model, for forecasting the RMB/USD exchange rate, and then discovers a better model for forecasting exchange rates. To find a better model, forecast value with accuracy, and uncertainty and model limitation are compared. We find that although SES model shows a better accuracy, the residual check and uncertainty shows ARIMA is a better forecast. Moreover, ARIMA is more complex and has relatively less limitations for RMB/USD exchange rate. Thus, Autoregressive Integrated Moving model is a better forecast compared with Simple Exponential Smoothing model.

Future Population Scenario In India And China – An Econometric Approach

Future Population Scenario In India And China – An Econometric Approach

Forecast of the Temperature Variation of an Elastohydrodynamic Contact by the Simple Exponential Smoothing Model (SES)

Forecast of the Temperature Variation of an Elastohydrodynamic Contact by the Simple Exponential Smoothing Model (SES)

Contribution to some aspects of the digestive tract anatomy and food evacuation of the Pacific fat sleeper Dormitator latrifrons

The development of feeding protocols and the study of gastric evacuation or passage of food through the digestive tract is essential to generate information on the digestive physiology of organisms, as they help to quantify the daily ration to avoid any poor diet. In this sense, the present work aims to demonstrate some basic aspects of the fish Dormitator latifrons digestive physiology by evaluating the transit of food through the intestinal tract as a function of time. In a completely randomized standard design, juveniles of D. latifrons were fed ad libitum with commercial pellets. Five conditioned organisms were sacrificed every hour after a single feeding. The food bolus was collected at each sampling point, using polynomial regression models to analyze the passage of food through time in the digestive tract. The passage of food showed a nonlinear behavior in time, being the simple exponential decay model of two parameters (R2 = 0.630; P < 0.0001) the best to explain the evacuation of food through the digestive tract. This knowledge will generate better feeding protocols for this species with commercial importance and aquaculture potential.

Estimation in Linear-Rate Simple Survival Models with Measurement Errors and Censoring

A simple exponential regression model is considered where the rate parameter of the response variable linearly depends on the explanatory variable. We consider complications of the model: censoring of the response variable (either upper censoring or interval observations), the additive classical error or multiplicative Berkson error in the explanatory variable, or a combination of censoring with Berkson errors. We construct or use already-known estimators in the models, and verify their performance in simulations.

Estimating the Chlorophyll-a in the Nha Trang Bay using Landsat-8 OLI data

The pigment chlorophyll-a (Chl-a) is used to evaluate aquatic ecological health. Using remote sensing techniques to estimate this pigment and spatially mapping its distribution becomes essential for measuring and assessing water quality in coastal areas. Nha Trang Bay is famous not only for its scenery but also for its biodiversity values, especially the existence of coral reefs. In this study, Landsat-8 OLI was taken on June 3, 2015, and field measurements of Chl-a at 13 survey sites from June 6-8, 2015, were used to build a local algorithm to monitor the spatio-temporal distribution of Chl-a content in Nha Trang Bay. The ACOLITE processor was employed for the atmospheric modification of Landsat-8 OLI images to obtain atmospherically corrected surface reflectance products. Four types of simple regression models, including linear, exponential, logarithmic, and power models, were used to describe the relationship between the in-situ measurement of Chl-a and remote sensing reflectance of Landsat-8 OLI data. The results of correlation analysis have shown a statistically significant relationship between field measurement of Chl-a concentration and the remote sensing reflectance ratio of Landsat-8 band 3 versus band 2 using an exponential model with a coefficient of determination of 0.88 (p < 0.001) and root mean square error (RMSE) of 0.40 mg/m3. This empirical relationship was applied to map the spatial distribution of Chl-a concentration from 27 cloudless Landsat-8 OLI images from 2013 to 2021. The spatio-temporal distribution of Chl-a indicated that the Chl-a concentration in the Bay has a low value (less than 1 mg/m3). However, this concentration becomes high in the coastal areas (greater than 1 mg/m3) and the Cai and Tac rivers (greater than 2 mg/m3). It is also noted that the content of Chl-a in the rainy season is fairly higher than in the dry season, by an average of 0.68 mg/m3 and 0.53 mg/m3, respectively. This research highlights that Landsat-8 OLI data can be an effective and valuable tool in monitoring Chl-a for coastal areas.

Forecasting Using Point-valued Time Series and Fuzzy-valued Time Series Models

The point-valued time series (PTS) is simply about one value in each time or period of the data, but when the data have two values at each time, the suitable time series is called the interval-valued time series (ITS). An example of ITS is the daily close and open stock prices. If the time series data are of typed linguistic data, for example, “low increase”, “medium increase” and “high increase”, the time series data are called a fuzzy-valued time series or being well-known as fuzzy time series (FTS). The aim of this study is to compare the PTS and FTS models for forecasting stock prices in the stock market. The stock market is one of the essential investments in the economy. The movement of stock price can be leading or declining which respectively expands or contracts a country’s economy. The movement may also represent the scenario or event happening in a company. Therefore, forecasting stock price movement is highly important since it will assist investors and sellers to make planning in their investment’s decision. Therefore, the objectives of this study are to identify the best forecasting models in point-valued time series (PTS) and fuzzy time series (FTS) based on forecasting measurement error. Eight stock price forecasting models consisting of four models from PTS and four models from FTS. Meanwhile, four forecasting measurement errors were discussed in the analysis as a criterion for choosing the best forecasting model. A set of daily historical data from the Bursa Malaysia website was used as a basis for analysis. The finding shows the simple exponential smoothing model is the best PTS model. In the meantime, the Cheng model based on Sturges’ rule is the best FTS model. However, among these two types of models, the Cheng model is found to be the best model with forecasting measurement errors of 0.0001 (MSFE), 0.0108 (RMSFE) and 1.1918 (MAPFE). The results reveal that besides the PTS model, the FTS model is an alternative model to forecast stock market movement. Moreover, these FTS and PTS models can also be applied to solving other forecasting problems.

Acceleration length and time of falling spherical particles

Acceleration length and time are required for various aspects of system design. However, simple and accurate equations that can be used by designers are lacking. Therefore, in this study, we used previously conducted tests and the simple exponential model to describe particle velocity and acceleration and thereby calculate the acceleration length, which is the length required for a particle to achieve 99% of the terminal velocity. Results indicated that the acceleration length can be well described by a function of the Archimedes number, density ratio, and particle diameter. Several new tests were conducted to measure the particle velocity as a function of time. These tests confirmed the newly developed model that describes the particle location, velocity, and acceleration as functions of time. Based on the new model, the acceleration time was calculated and found to be well described by the acceleration length and terminal velocity.

An empirical elastic anisotropy prediction model in self-sourced reservoir shales and its influencing factor analysis

Organic shales can split into laminae or thin layers due to their fissility and are usually characterized as transverse isotropic media. Understanding their elastic and anisotropic properties plays a significant role in geophysical modeling and imaging, reservoir geomechanics, and reservoir characterization. One can obtain their velocity anisotropy through laboratory and field measurements or rock-physics models. However, the measurements are scarce, and theoretical modeling of Thomsen’s anisotropy parameters generally requires extra inputs, which are sometimes difficult to obtain. We have compiled ultrasonic data from 159 self-sourced reservoir shale samples from the literature and our own measurements. Simple exponential models can capture the trends of the P- and S-wave anisotropy parameters [Formula: see text] and [Formula: see text] decreasing with the increasing vertical P- and S-wave velocities, with [Formula: see text] being above 0.84. The error in estimated anisotropy is small for high-velocity ([Formula: see text]) shales but relatively large for low-velocity ([Formula: see text]) shales. We analyze the influence of multiple geologic factors on the proposed anisotropy-velocity relationships. We observe that velocity anisotropy generally increases with clay content. In addition, mature shales (0.6 &lt; Ro (%) &lt; 1.4) generally have stronger anisotropy strength spreading in a broader range than overmature shales (Ro (%) &gt; 1.4). Overall, no single parameter dominates the source of velocity anisotropy, which is jointly affected by the coupled factors of clay content, organic matter, porosity, maturity level, and geologic history. The empirical model has the potential to offer a fast and straightforward method of predicting P- and S-wave anisotropy strength from vertical P- and S-wave velocities.

A semiempirical transparency model for dual energy cargo radiography applications

Cargo containers passing through ports are scanned by non-intrusive inspection systems to search for concealed illicit materials. By using two photon beams with different energy spectra, dual energy inspection systems are sensitive to both the area density and the atomic number of cargo contents. Most literature on the subject assumes a simple exponential attenuation model for photon intensity in which only free streaming photons are detected. However, this approximation neglects second order effects such as scattering, leading to a biased model and thus incorrect material predictions. This work studies the accuracy of the free streaming model by comparing it to simulation outputs, finding that the model shows poor atomic number reconstruction accuracy at high-Z and suffers significantly if the source energy spectra and detector response function are not known exactly. To address these challenges, this work introduces a semiempirical transparency model which modifies the free streaming model by rescaling different components of the mass attenuation coefficient, allowing the model to capture secondary effects ignored by the free streaming model. The semiempirical model displays improvement agreement with simulated results at high-Z and shows excellent extrapolation to materials and thicknesses which were not included during the calibration step. Furthermore, this work demonstrates that the semiempirical model yields accurate atomic number predictions even when the source spectra and detector response are not known exactly. Using the semiempirical model, manufacturers can perform a simple calibration to enable more precise Z reconstruction capabilities, which has the potential to significantly improve the performance of existing dual energy radiographic systems.

Statistical modelling and forecasting annual sugarcane production in India: Using various time series models

AbstractThis paper proposes a comparison of various time series forecasting models to forecast annual data on sugarcane production over 63 years from 1960 to 2022. In this research, the Mean Forecast Model, the Naive Model, the Simple Exponential Smoothing Model, Holt's model, and the Autoregressive Integrated Moving Average time series models have all been used to make effective and accurate predictions for sugarcane. Different scale‐dependent error forecasting techniques and residual analysis have been used to examine the forecasting accuracy of these time series models. SE of Residuals, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Akaike's Information Criterion (AIC) are used to analyse the forecast's accuracy. The best model has been selected based on the predictions with the lowest value, according to the three‐performance metrics of RMSE, MAE, and AIC. The estimated sugarcane production shows an increasing trend for the next 10 years and is projected to be 37,763.38 million tonnes in the year 2032. Further, empirical results support the plan and execution of viable strategies to advance sugarcane production in India to fulfil the utilisation need of the increasing population and further improve food security.

Review of contemporary cetacean stock assessment models

Model-based methods of analysis are widely used to conduct cetacean stock assessments, and to provide the operating models on which management strategy evaluation is based. This paper reviews recent assessments and management strategy evaluations for cetacean populations, with a view towards establishing ‘best practice’ guidelines for such analyses. The models on which these analyses are based range from simple exponential trend models that ignore density-dependence to complex multi-stock age-sex- and stage-structured models that form the basis for management strategy evaluation. Most analyses assume that density-dependence is on calf survival (which implicitly includes maturity and pregnancy rate), but it could also impact the survival rate of adults or the age-at-maturity. Cetaceans seldom have more than one calf per female each year, which limits the variation in calf numbers, and places an upper limit on the effects of density-dependent calf survival. The models differ in terms of whether the population projections start when substantial catches first occurred or whether allowance is made for time-varying carrying capacity by starting the model in a more recent year. Most of the models are deterministic, but account needs to be taken of variation in cohort strength for analyses that include age-composition data or for species that are relatively short-lived. A limited number of analyses include process variability using a statespace-like modelling framework. For some stocks, abundance is so low that ideally both demographic and environmental variability should be included in models. The primary source of data for parameter estimation is a time-series of estimates of absolute abundance, although some approaches considered used a variety of data types, including relative abundance indices, mark-recapture data and minimum abundance estimates based on haplotype counts. In general, at least one estimate of absolute abundance is needed for parameter estimation; this is because there is a lack of catch-induced declines in abundance captured by indices of relative abundance that could be used to provide information on absolute abundance. Similarly, information on abundance from age- and length- composition data is limited. Most of the analyses quantify uncertainty using Bayesian methods to allow information on biological parameters, particularly the intrinsic rate of growth and the relative population at which maximum production occurs, to be included in the analyses, along with sensitivity testing. The future for the models on which assessments and management strategy evaluations are based will often involve multi-stock models that include age-, sex- and spatial-structure and are fitted as state-space formulations, although at present such models are often too computationally intensive to be feasible for implementation or there is insufficient information in the data to estimate the parameters representing all the processes, leading to simplifications, with the result that the performance of some of the methods of assessment used for cetacean stocks needs to be better understood, including through simulation testing.

Time series modeling and forecasting of vegetable production in United Arab Emirates

Crop forecasting is a formidable challenge. The national and state governments need such predictions before harvesting for various policy decisions relating to storage, distribution, pricing, marketing, importexport etc. In this paper, univariate forecasting models such as random walk, linear trend, quadratic trend, exponential trend, S-curve trend, simple exponential smoothing, Holt's linear exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) models are used to predict vegetable production in the United Arab Emirates. For empirical analysis, a set of 9 different vegetable groups have been considered, contingent upon availability of required data. Annual data from 1974–75 to 2018–19 was used to forecast the next five years since 2019. Suitable models were selected based on the lowest RMSE and minimum of AIC criterion. Model diagnostic checking was done through Runs above and below the median, Runs up and down and Ljung-Box tests on ACF and PACF of residual terms. For onions and green shallots linear trend model was selected as the best fit, whereas simple exponential smoothing model was most suitable in cauliflowers and broccoli, pumpkins, squash and gourds and spinach. The optimum model obtained for forecasting carrots and turnips was Holt's linear exponential smoothing model and ARIMA model was the best fit for the rest of vegetable groups.

An Overview of Performance Measurement for Demand Forecasting Based on Artificial Neural Networks

Demand forecasting is an essential task to match supply and demand. From a supplier’s view, demand forecasting is important to optimize supply chains and thus maximize profits. The ever-increasing availability of data that can be used as input factors for predictive models allows more and more sophistication for diverse forecasting tasks in the context of demand forecasting. On the one hand, increasingly complex models have been used for demand forecasting over the last years, from simple exponential smoothing methods and ARIMA models up to complex, hybrid (deep) artificial neural networks. On the other hand, little attention is paid to the methods that evaluate the forecasting performance of these models, which are essential for the selection from among potential forecasting models. In this article, we aim to answer the question of what are the most favourable measurements in recent literature on applied neural network demand forecasting for supply chain management. To this end, we analyzed 193 relevant publications in which demand forecasting was applied using artificial neural networks. We found that in artificial neural network demand forecasting used to evaluate forecasting performance, Mean Absolute Percentage Error, Root Mean Squared Error, Mean Squared Error and Mean Absolute Error are by far the most popular methods. Furthermore, we found that when forecasting performance measurements are combined, the most common combination is the combination of Mean Absolute Error, the Root Mean Squared Error and the Mean Absolute Error.

Predicting daily emergency department visits using machine learning could increase accuracy

ObjectiveAdministrators and clinicians alike have attempted to predict emergency department visits for many years. The ability to predict or “forecast” ED visit volume can allow for more efficient resource allocation, including up-staffing or down-staffing, changing OR schedules, and predicting the need for significant resources. The goal of this study is to examine combinations of variables via machine learning to increase prediction accuracy and determine the factors that are most predictive of overall ED visits. As compared to a simple univariate time series model, we hypothesize that machine learning models will predict St. Joseph Mercy Ann Arbor's patient visit load for the emergency department (ED) with higher accuracy than a simple univariate time series model. MethodsUnivariate time series models for daily ED visits, including ARIMA, Exponential Smoothing (ETS), and Facebook Inc.'s prophet algorithm were estimated as a baseline comparison. Machine learning models, including random forests and gradient boosted machines (GBM), were trained using data from 2017 to 2018. After final models were created, they were applied to the 2019 data to determine how well these models predicted actual ED patient volumes in data not utilized during the model fitting process. The accuracy of the machine learning and time series models were assessed based on out-of-sample predictive accuracy, compared using root mean squared error (RMSE). ResultsUsing root mean squared error (RMSE) to assess out-of-sample predictive accuracy of the models, the results showed that the random forest model was the most accurate at predicting daily ED visits in the 2019 test set, followed by the GBM model. These performed only slightly better than the simple exponential smoothing model predictions. The ARIMA model performed poorly in comparison. The day of the week (likely capturing differences between weekdays and weekends) was found to be the most important predictor of patient volumes. Weather-related features such as maximum temperature and SFC pressure appeared to capture some of the seasonality trends related to changes in patient volumes. ConclusionsMachine learning models perform better at predicting daily patient volumes as compared to simple univariate time series models, though not by a substantial amount. Further research can help confirm these limited initial results. Gathering more training data and additional feature engineering could also be beneficial to training the models and potentially improving predictive accuracy.

A Unified Nonlinear Elastic Model for Rock Material

Under conditions of low or medium stress, rocks that are in the compression state exhibit perceivable nonlinear elastic characteristics. After a comprehensive review of the existing nonlinear elastic models of rocks and joints, we proposed a new unified nonlinear elastic model. This new model contains two parameters with clear definitions, namely, the initial elastic modulus Eni and the modulus change rate m. This model covers a variety of existing models, including the simple exponential model, BB model and two-part Hooke’s model, etc. Based on the RMT experimental system, a large number of uniaxial compression tests for dolomite, granite, limestone and sandstone were performed, and their nonlinear deformation stress‒strain curves were obtained and fit with the unified nonlinear elastic model. The results show that the rocks have obvious nonlinear elastic characteristics in their initial compression stage, and that the unified nonlinear elastic model is able to describe these phenomena rather well. In addition, an empirical formula for predicting the uniaxial compressive strength of the rock was constructed, corresponding to the unified nonlinear elastic model.

VOLUME TABLE OF Acacia auriculiformis A. CUNN. EX BENTH. AND Acacia mangium WILLD. PLANTED IN WOODLOT OF SOCIAL FORESTRY PROJECT IN DHAKA FOREST DIVISION OF BANGLADESH

Tree volume, the traditional measurement of wood quantity is a function of trees’ height, basal area, shape, and bark thickness. Tree volume table is the statement of expected tree volume of nominated dimensions in a particular forest stand or population. Acacia auriculiformis A. Cunn. ex. Benth. and Acacia mangium Willd. are two fast growing and hardy leguminous (Leguminosae, Mimosaceae) exotic species have widely been planted in the woodlot of Asian Development Bank financed Social Forestry Project in Bangladesh. In this study, models for calculating tree stem volume and a two way volume tables for both A. auriculiformis and A. mangium were developed. Data were collected from the social forestry woodlot of the respective species in Dhaka Forest Division, Bangladesh. Simple linear, logarithmic, polynomial, power and exponential regression models were developed. Best-fit model was selected based on highest R2 among seven linear regression models for volume calculation of A. auriculiformis and A. mangium. Regression R2 of the simple linear regression model for both the species was either higher or at least equal in compararison with said logarithmic, polynomial, power, and exponential regression models. Therefore, simple linear regression model as a function of height (h) and diameter at breast height (dbh) were adopted for the preparation of two way local volume table for both the species due to its simplicity in application.